Energy Modeling and Techno-Economic Feasibility Analysis of Greenhouses for Tomato Cultivation Utilizing the Waste Heat of Cryptocurrency Miners
Bibliographic record
Abstract
Greenhouses extend growing seasons in upper latitudes to provide fresh, healthy food. Costs associated with carbon-emission-intensive natural gas heating, however, limit greenhouse applications and scaling. One approach to reducing greenhouse heating costs is electrification by using waste heat from cryptocurrency miners. To probe this potential, a new quasi-steady state thermal model is developed to simulate the thermal interaction between a greenhouse and the environment, thereby estimating the heating and cooling demands of the greenhouse. A cryptocurrency mining system was experimentally evaluated for heating potential. Using these experimental values, the new thermal model was applied to the waste heat of the three cryptocurrency mining systems (1, 50, and 408 miners) for optimally sized greenhouses in six locations in Canada and the U.S.: Alberta, Ontario, Quebec, California, Texas, and New York. A comprehensive parametric study was then used to analyze the effect of various parameters (air exchange rate, planting area, lighting allowance factor, and photoperiod) on the thermal demands and optimal sizing of greenhouses. Using waste heat from cryptocurrency mining was found to be economically profitable to offset natural gas heating depending on the utility rates and Bitcoin value in a wide range of scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".